How do you choose the adjusting p-value?
The simplest way to adjust your P values is to use the conservative Bonferroni correction method which multiplies the raw P values by the number of tests m (i.e. length of the vector P_values).
How does the Bonferroni correction work?
Understanding the Bonferroni Test The Bonferroni test, also known as “Bonferroni correction” or “Bonferroni adjustment” suggests that the p-value for each test must be equal to its alpha divided by the number of tests performed. The test is performed by taking a random sample of a population or group.
What is the adjusted p-value in multiple comparisons?
What is the adjusted p-value in multiple comparisons? Use for multiple comparisons in ANOVA, the adjusted p-value indicates which factor level comparisons within a family of comparisons (hypothesis tests) are significantly different. If the adjusted p-value is less than alpha, then you reject the null hypothesis.
When to use adjusted p value in ANOVA?
Use for multiple comparisons in ANOVA, the adjusted p-value indicates which factor level comparisons within a family of comparisons (hypothesis tests) are significantly different. If the adjusted p-value is less than alpha, then you reject the null hypothesis.
When to use the adjusted p-value for null hypothesis?
The adjusted p-value also represents the smallest family error rate at which a particular null hypothesis will be rejected. It is important to consider the family error rate when making multiple comparisons because your chances of committing a type I error for a series of comparisons is greater than the error rate for any one comparison alone.
What should the adjusted p be for the significance level?
The first time you set the familywise significance level to 5%. The second time, you set it to 1% level. If a particular comparison is statistically significant by the first calculations (5% significance level) but is not for the second (1% significance level), its adjusted P value must be between 0.01 and 0.05, say 0.0323.